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"""
Neo4j Knowledge Graph Analytics
Performs analysis on the CVE knowledge graph and generates insights.
"""
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from neo4j import GraphDatabase
import pandas as pd
from datetime import datetime
import json
class GraphAnalytics:
def __init__(self, uri="bolt://localhost:7687", user="neo4j", password="password"):
self.driver = GraphDatabase.driver(uri, auth=(user, password))
def close(self):
self.driver.close()
def run_query(self, query, parameters=None):
"""Execute a Cypher query and return results"""
with self.driver.session() as session:
result = session.run(query, parameters or {})
return [record.data() for record in result]
def get_basic_stats(self):
"""Get basic statistics about the knowledge graph"""
print("Basic Knowledge Graph Statistics")
print("=" * 50)
# Node counts
node_stats = self.run_query("""
MATCH (n)
RETURN labels(n) as NodeType, count(n) as Count
ORDER BY Count DESC
""")
print("\nNode Counts:")
for stat in node_stats:
node_type = stat['NodeType'][0] if stat['NodeType'] else 'Unknown'
print(f" {node_type}: {stat['Count']:,}")
# Relationship counts
rel_stats = self.run_query("""
MATCH ()-[r]->()
RETURN type(r) as RelationshipType, count(r) as Count
ORDER BY Count DESC
""")
print("\nRelationship Counts:")
for stat in rel_stats:
print(f" {stat['RelationshipType']}: {stat['Count']:,}")
def get_cve_analysis(self):
"""Analyze CVE data"""
print("\nCVE Analysis")
print("=" * 50)
# CVE distribution by year
cve_by_year = self.run_query("""
MATCH (cve:CVE)
WITH cve, split(cve.id, '-')[1] as year
RETURN year as Year, count(cve) as CVECount
ORDER BY year DESC
""")
print("\nCVE Distribution by Year:")
for stat in cve_by_year:
print(f" {stat['Year']}: {stat['CVECount']:,} CVEs")
# CVE distribution by severity
cve_by_severity = self.run_query("""
MATCH (cve:CVE)
WHERE cve.cvss_v3_severity IS NOT NULL
RETURN cve.cvss_v3_severity as Severity, count(cve) as CVECount
ORDER BY CVECount DESC
""")
print("\nCVE Distribution by Severity:")
for stat in cve_by_severity:
print(f" {stat['Severity']}: {stat['CVECount']:,} CVEs")
# Top vendors by CVE count
top_vendors = self.run_query("""
MATCH (cve:CVE)-[:AFFECTS]->(product:Product)-[:MANUFACTURED_BY]->(vendor:Vendor)
RETURN vendor.name as Vendor, count(DISTINCT cve) as CVECount
ORDER BY CVECount DESC
LIMIT 10
""")
print("\nTop Vendors by CVE Count:")
for i, vendor in enumerate(top_vendors, 1):
print(f" {i}. {vendor['Vendor']}: {vendor['CVECount']:,} CVEs")
# Top products by CVE count
top_products = self.run_query("""
MATCH (cve:CVE)-[:AFFECTS]->(product:Product)-[:MANUFACTURED_BY]->(vendor:Vendor)
RETURN product.name as Product, vendor.name as Vendor, count(cve) as CVECount
ORDER BY CVECount DESC
LIMIT 10
""")
print("\nTop Products by CVE Count:")
for i, product in enumerate(top_products, 1):
print(f" {i}. {product['Product']} ({product['Vendor']}): {product['CVECount']:,} CVEs")
# Most common CWEs
top_cwes = self.run_query("""
MATCH (cve:CVE)-[:HAS_WEAKNESS]->(cwe:CWE)
RETURN cwe.id as CWE, count(cve) as CVECount
ORDER BY CVECount DESC
LIMIT 10
""")
print("\nMost Common Weaknesses (CWE):")
for i, cwe in enumerate(top_cwes, 1):
print(f" {i}. {cwe['CWE']}: {cwe['CVECount']:,} CVEs")
def get_vendor_ecosystem_analysis(self):
"""Analyze vendor product ecosystems"""
print("\nVendor Ecosystem Analysis")
print("=" * 50)
# Vendors with most products
vendors_by_products = self.run_query("""
MATCH (vendor:Vendor)<-[:MANUFACTURED_BY]-(product:Product)
RETURN vendor.name as Vendor, count(DISTINCT product) as ProductCount
ORDER BY ProductCount DESC
LIMIT 15
""")
print("\nVendors by Product Count:")
for i, vendor in enumerate(vendors_by_products, 1):
print(f" {i}. {vendor['Vendor']}: {vendor['ProductCount']:,} products")
# Most vulnerable vendors (CVEs per product)
vulnerable_vendors = self.run_query("""
MATCH (cve:CVE)-[:AFFECTS]->(product:Product)-[:MANUFACTURED_BY]->(vendor:Vendor)
WITH vendor, count(DISTINCT cve) as cveCount, count(DISTINCT product) as productCount
WHERE productCount > 0
RETURN vendor.name as Vendor, cveCount as CVEs, productCount as Products,
toFloat(cveCount) / productCount as CVEsPerProduct
ORDER BY CVEsPerProduct DESC
LIMIT 15
""")
print("\nMost Vulnerable Vendors (CVEs per Product):")
for i, vendor in enumerate(vulnerable_vendors, 1):
print(f" {i}. {vendor['Vendor']}: {vendor['CVEsPerProduct']:.2f} CVEs/product "
f"({vendor['CVEs']:,} CVEs, {vendor['Products']:,} products)")
def get_attack_pattern_analysis(self):
"""Analyze attack patterns and weaknesses"""
print("\nAttack Pattern Analysis")
print("=" * 50)
# Most common attack patterns
top_capecs = self.run_query("""
MATCH (cve:CVE)-[:USES_PATTERN]->(capec:CAPEC)
RETURN capec.id as CAPEC, count(cve) as CVECount
ORDER BY CVECount DESC
LIMIT 10
""")
print("\nMost Common Attack Patterns (CAPEC):")
for i, capec in enumerate(top_capecs, 1):
print(f" {i}. {capec['CAPEC']}: {capec['CVECount']:,} CVEs")
# CVE-CWE-CAPEC relationships
cwe_capec_relationships = self.run_query("""
MATCH (cve:CVE)-[:HAS_WEAKNESS]->(cwe:CWE)
MATCH (cve)-[:USES_PATTERN]->(capec:CAPEC)
RETURN cwe.id as CWE, capec.id as CAPEC, count(cve) as CVECount
ORDER BY CVECount DESC
LIMIT 10
""")
print("\nTop CWE-CAPEC Combinations:")
for i, rel in enumerate(cwe_capec_relationships, 1):
print(f" {i}. {rel['CWE']} + {rel['CAPEC']}: {rel['CVECount']:,} CVEs")
def get_version_analysis(self):
"""Analyze version information"""
print("\nVersion Analysis")
print("=" * 50)
# Products with version information
products_with_versions = self.run_query("""
MATCH (product:Product)-[:HAS_VERSION]->(version:Version)
RETURN count(DISTINCT product) as ProductsWithVersions
""")
total_products = self.run_query("""
MATCH (product:Product)
RETURN count(product) as TotalProducts
""")
if products_with_versions and total_products:
version_coverage = (products_with_versions[0]['ProductsWithVersions'] /
total_products[0]['TotalProducts']) * 100
print(f"\nVersion Coverage: {version_coverage:.1f}% of products have version information")
# Products with multiple versions
multi_version_products = self.run_query("""
MATCH (product:Product)-[:HAS_VERSION]->(version:Version)
WITH product, count(version) as versionCount
WHERE versionCount > 1
RETURN product.name as Product, product.vendor as Vendor, versionCount
ORDER BY versionCount DESC
LIMIT 10
""")
print("\nProducts with Multiple Versions:")
for i, product in enumerate(multi_version_products, 1):
print(f" {i}. {product['Product']} ({product['Vendor']}): {product['versionCount']} versions")
def get_data_quality_report(self):
"""Generate data quality report"""
print("\nData Quality Report")
print("=" * 50)
# Products without vendors
products_without_vendors = self.run_query("""
MATCH (product:Product)
WHERE NOT (product)-[:MANUFACTURED_BY]->()
RETURN count(product) as Count
""")
if products_without_vendors:
print(f"\nWARNING: Products without vendor relationships: {products_without_vendors[0]['Count']:,}")
# CVEs without products
cves_without_products = self.run_query("""
MATCH (cve:CVE)
WHERE NOT (cve)-[:AFFECTS]->()
RETURN count(cve) as Count
""")
if cves_without_products:
print(f"WARNING: CVEs without product relationships: {cves_without_products[0]['Count']:,}")
# CVEs without CVSS scores
cves_without_cvss = self.run_query("""
MATCH (cve:CVE)
WHERE cve.cvss_v3_base_score IS NULL
RETURN count(cve) as Count
""")
if cves_without_cvss:
print(f"WARNING: CVEs without CVSS scores: {cves_without_cvss[0]['Count']:,}")
# Duplicate products
duplicate_products = self.run_query("""
MATCH (product:Product)
WITH product.name as name, product.vendor as vendor, collect(product) as products
WHERE size(products) > 1
RETURN count(name) as Count
""")
if duplicate_products:
print(f"WARNING: Products with potential duplicates: {duplicate_products[0]['Count']:,}")
def export_analytics_to_json(self, filename="graph_analytics.json"):
"""Export analytics data to JSON file"""
print(f"\nExporting analytics to {filename}")
analytics_data = {
"timestamp": datetime.now().isoformat(),
"basic_stats": {
"nodes": self.run_query("MATCH (n) RETURN labels(n) as NodeType, count(n) as Count"),
"relationships": self.run_query("MATCH ()-[r]->() RETURN type(r) as RelationshipType, count(r) as Count")
},
"cve_analysis": {
"by_year": self.run_query("MATCH (cve:CVE) WITH cve, split(cve.id, '-')[1] as year RETURN year as Year, count(cve) as CVECount ORDER BY year DESC"),
"top_vendors": self.run_query("MATCH (cve:CVE)-[:AFFECTS]->(product:Product)-[:MANUFACTURED_BY]->(vendor:Vendor) RETURN vendor.name as Vendor, count(DISTINCT cve) as CVECount ORDER BY CVECount DESC LIMIT 20"),
"top_products": self.run_query("MATCH (cve:CVE)-[:AFFECTS]->(product:Product)-[:MANUFACTURED_BY]->(vendor:Vendor) RETURN product.name as Product, vendor.name as Vendor, count(cve) as CVECount ORDER BY CVECount DESC LIMIT 20")
},
"vendor_analysis": {
"by_products": self.run_query("MATCH (vendor:Vendor)<-[:MANUFACTURED_BY]-(product:Product) RETURN vendor.name as Vendor, count(DISTINCT product) as ProductCount ORDER BY ProductCount DESC LIMIT 20"),
"vulnerability_ratio": self.run_query("MATCH (cve:CVE)-[:AFFECTS]->(product:Product)-[:MANUFACTURED_BY]->(vendor:Vendor) WITH vendor, count(DISTINCT cve) as cveCount, count(DISTINCT product) as productCount WHERE productCount > 0 RETURN vendor.name as Vendor, cveCount as CVEs, productCount as Products, toFloat(cveCount) / productCount as CVEsPerProduct ORDER BY CVEsPerProduct DESC LIMIT 20")
}
}
with open(filename, 'w') as f:
json.dump(analytics_data, f, indent=2)
print(f"SUCCESS: Analytics exported to {filename}")
def run_full_analysis(self):
"""Run complete analysis"""
print("Starting Enhanced Knowledge Graph Analytics")
print("=" * 60)
try:
self.get_basic_stats()
self.get_cve_analysis()
self.get_vendor_ecosystem_analysis()
self.get_attack_pattern_analysis()
self.get_version_analysis()
self.get_data_quality_report()
self.export_analytics_to_json()
print("\nSUCCESS: Analysis complete!")
except Exception as e:
print(f"ERROR: Error during analysis: {e}")
finally:
self.close()
def main():
"""Main function"""
import argparse
parser = argparse.ArgumentParser(description="Neo4j Knowledge Graph Analytics")
parser.add_argument("--uri", default="bolt://localhost:7687", help="Neo4j URI")
parser.add_argument("--user", default="neo4j", help="Neo4j username")
parser.add_argument("--password", default="password", help="Neo4j password")
parser.add_argument("--export", help="Export filename for JSON analytics")
args = parser.parse_args()
analytics = GraphAnalytics(args.uri, args.user, args.password)
if args.export:
analytics.export_analytics_to_json(args.export)
else:
analytics.run_full_analysis()
if __name__ == "__main__":
main() |